diff options
Diffstat (limited to 'lib/parameters.py')
| -rw-r--r-- | lib/parameters.py | 14 |
1 files changed, 7 insertions, 7 deletions
diff --git a/lib/parameters.py b/lib/parameters.py index 2e3878f..f367eb9 100644 --- a/lib/parameters.py +++ b/lib/parameters.py @@ -918,7 +918,7 @@ class ModelAttribute: :param data: Measurements. [data 1, data 2, data 3, ...] :param with_function_leaves: Use fitted function sets to generate function leaves for scalar parameters :param with_nonbinary_nodes: Allow non-binary nodes for enum and scalar parameters (i.e., nodes with more than two children) - :param with_sklearn_cart: Use `sklearn.tree.DecisionTreeRegressor` CART implementation for tree generation. Does not support categorial (enum) + :param with_sklearn_cart: Use `sklearn.tree.DecisionTreeRegressor` CART implementation for tree generation. Does not support categorical (enum) and sparse parameters. Both are ignored during fitting. All other options are ignored as well. :param with_sklearn_decart: Use `sklearn.tree.DecisionTreeRegressor` CART implementation in DECART mode for tree generation. CART limitations apply; additionaly, scalar parameters are ignored during fitting. @@ -928,8 +928,8 @@ class ModelAttribute: :returns: SplitFunction or StaticFunction """ - categorial_to_scalar = bool( - int(os.getenv("DFATOOL_PARAM_CATEGORIAL_TO_SCALAR", "0")) + categorical_to_scalar = bool( + int(os.getenv("DFATOOL_PARAM_CATEGORICAL_TO_SCALAR", "0")) ) if with_function_leaves is None: with_function_leaves = bool( @@ -969,13 +969,13 @@ class ModelAttribute: fit_parameters, category_to_index, ignore_index = param_to_ndarray( parameters, with_nan=False, - categorial_to_scalar=categorial_to_scalar, + categorical_to_scalar=categorical_to_scalar, ) elif with_sklearn_decart: fit_parameters, category_to_index, ignore_index = param_to_ndarray( parameters, with_nan=False, - categorial_to_scalar=categorial_to_scalar, + categorical_to_scalar=categorical_to_scalar, ignore_indexes=self.scalar_param_indexes, ) if fit_parameters.shape[1] == 0: @@ -1071,7 +1071,7 @@ class ModelAttribute: reg_lambda=reg_lambda, ) fit_parameters, category_to_index, ignore_index = param_to_ndarray( - parameters, with_nan=False, categorial_to_scalar=categorial_to_scalar + parameters, with_nan=False, categorical_to_scalar=categorical_to_scalar ) if fit_parameters.shape[1] == 0: logger.warning( @@ -1159,7 +1159,7 @@ class ModelAttribute: criterion=criterion, ) fit_parameters, category_to_index, ignore_index = param_to_ndarray( - parameters, with_nan=False, categorial_to_scalar=categorial_to_scalar + parameters, with_nan=False, categorical_to_scalar=categorical_to_scalar ) if fit_parameters.shape[1] == 0: logger.warning( |